Quant interviews are an important part of the recruiting process for quantitative roles in investment banks. Candidates are tested on their technical skills in programming, statistics, probability, derivatives pricing, financial modeling etc. Preparing a good set of quant interview questions is key to performing well. In this article, we summarize some of the most common quant interview questions found in PDF guides and list key conclusions from each major section. There are sections on C++, python, probability, stochastic processes, options pricing, brainteasers etc. Understanding the core concepts and being able to demonstrate problem solving skills under time pressure is critical to getting quant offers.
C++ section tests data structures, algorithms, coding
The C++ section focuses on data structures, algorithms and coding skills. Interviewers will ask candidates to code up solutions on the spot to problems involving arrays, linked lists, trees, graphs, sorting, searching etc. Questions test both conceptual knowledge and ability to write bug-free code quickly. Key takeaways include mastering core data structures, common algorithms, asymptotic notation, modular coding and good debugging skills. Candidates should solve many Leetcode problems and other online judges for practice.
Python questions cover NumPy, SciPy, Pandas, modeling
Python is widely used in quantitative finance for data analysis, modeling and visualization. Expect questions on NumPy for numerical programming, SciPy for scientific computing, Pandas for data manipulation and plotting with Matplotlib. Interviewers will test comfort with Python data structures, functions, OOP. Being able to cleanly pre-process data, run statistical analysis, build financial models and backtest strategies will be assessed. Make sure to get hands-on experience with Python modules like Numpy, Scipy, Pandas, scikit-learn, statsmodels before interviews.
Probability and stochastic processes form probability basis
Having a strong grasp of probability and stochastic processes will be assumed. Probability questions will cover Bayes’ theorem, random variables, expected value, variance, covariance, central limit theorem. Stochastic processes questions involve Brownian motion, Ito’s lemma, stochastic differential equations, Monte Carlo simulations etc. Know common probability distributions and their properties. Brush up on martingales, Markov chains. This form the theoretical foundations for derivatives pricing and risk management.
Derivatives pricing questions test models implementation
Derivatives pricing is a major component, with questions on implementing pricing models in C++ or Python. Be familiar with Black-Scholes-Merton, binomial trees, Monte Carlo methods for vanilla and exotic options. Prepare for questions on Greeks, implied volatility, risk-neutral valuation, put-call parity. Understand fixed income products, interest rate models like Vasicek, CIR. Be able to price derivatives while handling real world issues like discrete dividends, American exercise, barriers.
Brainteasers evaluate business judgement, logic, creativity
Brainteaser questions aim to evaluate general problem solving skills, business judgement, logic and creativity. Practice breaking down complex problems, making reasonable assumptions to simplify issues and articulating your thought process clearly. Useful techniques involve drawing diagrams, numerical estimation, pattern finding, divide and conquer approaches. Maintain a cool head, ask clarifying questions and think through examples to get insights.
In summary, quant interview preparation requires dedicated practice across coding, statistics, probability, derivatives and brainteasers. Mastering core technical skills, being able to think logically and creatively under time pressure are key to successfully getting quant offers. Keep practicing problems from online resources in addition to going through quant interview PDF guides.